Learning Vision-based Robotic Manipulation Tasks Sequentially in Offline Reinforcement Learning Settings

TL;DR

This paper explores sequential learning of robotic manipulation tasks using offline reinforcement learning, focusing on regularisation methods like synaptic intelligence to mitigate forgetting and transfer knowledge.

Contribution

It evaluates the effectiveness of regularisation-based continual learning methods in offline RL for robotic manipulation, addressing challenges like forgetting and knowledge transfer.

Findings

Sequential learning propagates previous knowledge, reducing new task learning time.

Synaptic intelligence mitigates catastrophic forgetting but offers limited knowledge transfer.

Task ordering and object configurations influence learning performance.

Abstract

With the rise of deep reinforcement learning (RL) methods, many complex robotic manipulation tasks are being solved. However, harnessing the full power of deep learning requires large datasets. Online-RL does not suit itself readily into this paradigm due to costly and time-taking agent environment interaction. Therefore recently, many offline-RL algorithms have been proposed to learn robotic tasks. But mainly, all such methods focus on a single task or multi-task learning, which requires retraining every time we need to learn a new task. Continuously learning tasks without forgetting previous knowledge combined with the power of offline deep-RL would allow us to scale the number of tasks by keep adding them one-after-another. In this paper, we investigate the effectiveness of regularisation-based methods like synaptic intelligence for sequentially learning image-based robotic manipulation tasks in an offline-RL setup. We evaluate the performance of this combined framework against common challenges of sequential learning: catastrophic forgetting and forward knowledge transfer. We performed experiments with different task combinations to analyze the effect of task ordering. We also investigated the effect of the number of object configurations and density of robot trajectories. We found that learning tasks sequentially helps in the propagation of knowledge from previous tasks, thereby reducing the time required to learn a new task. Regularisation based approaches for continuous learning like the synaptic intelligence method although helps in mitigating catastrophic forgetting but has shown only limited transfer of knowledge from previous tasks.